| Name | bcitoolbox JSON |
| Version |
0.1.0.6
JSON |
| download |
| home_page | None |
| Summary | A zero-programming package for Bayesian causal inference model |
| upload_time | 2025-07-14 21:41:18 |
| maintainer | None |
| docs_url | None |
| author | evans.zhu |
| requires_python | None |
| license | None |
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No requirements were recorded.
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BCI Toolbox is a Python implementation of the hierarchical Bayesian Causal Inference (BCI) model for multisensory research. BCI model is a statistical framework for understanding the causal relationships between sensory inputs and prior expectations of a common cause, which can account for human perception in a number of tasks, including temporal numerosity judgment (Shams et al., 2005; Wozny et al., 2008), spatial localization judgment (Körding et al., 2007; Wozny & Shams, 2011), size-weight illusion paradigm (Peters et al., 2016), rubber-hand illusion paradigm (Chancel et al., 2022; Chancel & Ehrsson, 2023).
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